Takeuchi T
Department of Legal Medicine, Nihon University School of Dentistry, Tokyo, Japan.
Nihon Hoigaku Zasshi. 1993 Jun;47(3):239-49.
Fuzzy inference has received much attention recently as a new type of computer control technology for application in various fields. In this study I applied fuzzy inference to personal identification in medical jurisprudence. Sex determination program was constructed from teeth using the fuzzy inference technique and was compared with known discriminant analysis. The materials examined were mandibular and maxillary dental plaster models of 100 adult males and females, and 80 infant males and females. For the permanent teeth, measurements were made on crown width and thickness of the mandibular and maxillary central incisors, canines, first premolars, and first molars, and of the width and length of the maxillary dental arch. For the deciduous teeth, measurements were made on crown width and thickness of all teeth. Stepwise discriminant analysis was conducted using these values. The values from 5 selected upper-ranked items were set as input objects of the fuzzy inference program and the probability of maleness obtained from the discriminant analysis was set as the output object. Finally, sex determination program for both permanent and deciduous teeth was constructed using a fuzzy inference software development tool. Each measured value was input into this program and the output results were compared with those of discriminant analysis. The percentages of correct determinations for permanent teeth were 83.0% for males and 86.0% for females using stepwise discriminant analysis, however it increased to 93.0% for males and 89.0% for females using the fuzzy inference program. The percentages of correct determinations for deciduous teeth models were 67.5% for males and 75.0% for females using stepwise discriminant analysis, and increased to 86.3% for males and 81.3% for females using the fuzzy inference program. Among samples with probabilities between 40% and 60%, 3 out of 14 males and 4 out of 13 females were misjudged using stepwise discriminant analysis in cases of permanent teeth. However using fuzzy inference program, it reduced to 0 for males and 3 for females. In case of deciduous teeth, 9 out of 20 males and 9 out of 23 females were misjudged using stepwise discriminant analysis. But it reduced to 5 for males and 4 for females using the fuzzy inference program.
模糊推理作为一种新型的计算机控制技术，近年来在各个领域的应用中受到了广泛关注。在本研究中，我将模糊推理应用于法医学中的个人识别。利用模糊推理技术构建了基于牙齿的性别判定程序，并与已知的判别分析方法进行了比较。所检查的材料是100名成年男性和女性以及80名婴幼儿男性和女性的下颌和上颌牙齿石膏模型。对于恒牙，测量了下颌和上颌中切牙、尖牙、第一前磨牙和第一磨牙的冠宽和厚度，以及上颌牙弓的宽度和长度。对于乳牙，测量了所有牙齿的冠宽和厚度。使用这些值进行逐步判别分析。将5个选定的排名靠前项目的值设置为模糊推理程序的输入对象，并将判别分析得出的男性概率设置为输出对象。最后，使用模糊推理软件开发工具构建了恒牙和乳牙的性别判定程序。将每个测量值输入该程序，并将输出结果与判别分析的结果进行比较。使用逐步判别分析，恒牙的正确判定百分比男性为83.0%，女性为86.0%，然而使用模糊推理程序时，男性增加到93.0%，女性增加到89.0%。乳牙模型使用逐步判别分析的正确判定百分比男性为67.5%，女性为75.0%，使用模糊推理程序时，男性增加到86.3%，女性增加到81.3%。在概率介于40%至60%之间的样本中，恒牙情况下使用逐步判别分析，14名男性中有3名、13名女性中有4名被误判。然而使用模糊推理程序时，男性降至0，女性降至3。在乳牙情况下，使用逐步判别分析，20名男性中有9名、23名女性中有9名被误判。但使用模糊推理程序时，男性降至5，女性降至4。
